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0.5.0
  • QuickStart
    • Command line interface (CLI)
    • Python
  • Installation
    • Docker Images
  • General concepts
    • Key Concepts
  • Configuration file
    • Variables
    • Training
      • Train config
      • Train Parameters
        • Metrics
      • DatasetReader
      • DataLearningIterator and DataFittingIterator
    • Inference
    • Model Configuration
      • Preprocessors
      • Tokenizers
      • Embedders
      • Vectorizers

Features

  • Overview
    • Models
      • NER model [docs]
      • Slot filling models [docs]
      • Classification model [docs]
      • Automatic spelling correction model [docs]
      • Ranking model [docs]
      • TF-IDF Ranker model [docs]
      • Question Answering model [docs]
      • Morphological tagging model [docs]
      • Frequently Asked Questions (FAQ) model [docs]
    • Skills
      • Goal-oriented bot [docs]
      • Seq2seq goal-oriented bot [docs]
      • eCommerce bot [docs]
      • ODQA [docs]
    • AutoML
      • Hyperparameters optimization [docs]
    • Embeddings
      • Pre-trained embeddings [docs]
    • Examples of some models
  • Models
    • BERT-based models
      • BERT for Classification
      • BERT for Named Entity Recognition (Sequence Tagging)
      • BERT for Context Question Answering (SQuAD)
      • BERT for Ranking
      • Using custom BERT in DeepPavlov
    • Context Question Answering
      • Task definition
      • Models
        • BERT
        • R-Net
      • Configuration
      • Prerequisites
      • Model usage from Python
      • Model usage from CLI
        • Training
        • Interact mode
      • Pretrained models:
        • SQuAD
        • SQuAD with contexts without correct answers
        • SDSJ Task B
    • Classification
      • Quick start
        • Command line
        • Python code
      • BERT models
      • Neural Networks on Keras
      • Sklearn models
      • Pre-trained models
      • How to train on other datasets
      • Comparison
      • How to improve the performance
      • References
    • Morphological Tagger
      • Usage examples.
        • Python:
        • Command line:
        • Task description
        • Algorithm description
        • Model configuration.
    • Named Entity Recognition
      • Train and use the model
      • Multilingual BERT Zero-Shot Transfer
      • NER task
      • Training data
      • Few-shot Language-Model based
      • Literature
    • Neural Ranking
      • Training and inference models on predifined datasets
        • BERT Ranking
        • Building your own response base for bert ranking
        • Ranking
        • Paraphrase identification
        • Paraphraser.ru dataset
        • Quora question pairs dataset
      • Training and inference on your own data
        • Ranking
        • Paraphrase identification
    • Slot filling
      • Configuration of the model
        • Dataset Reader
        • Dataset Iterator
        • Chainer
      • Usage of the model
      • Slotfilling without NER
    • Spelling Correction
      • Quick start
      • levenshtein_corrector
        • Component config parameters:
      • brillmoore
        • Component config parameters:
        • Training configuration
      • Language model
      • Comparison
    • TF-IDF Ranking
      • Quick Start
      • Configuration
      • Running the Ranker
        • Training
        • Interacting
      • Available Data and Pretrained Models
        • enwiki.db
        • enwiki_tfidf_matrix.npz
        • ruwiki.db
        • ruwiki_tfidf_matrix.npz
      • Comparison
      • References
    • Popularity Ranking
      • Quick Start
      • Configuration
      • Running the Ranker
        • Interacting
      • Available Data and Pretrained Models
      • References
    • Knowledge Base Question answering
      • Description
      • Use the model
  • Skills
    • Goal-Oriented Dialogue Bot
      • Intro
      • Usage
        • Requirements
        • Configs:
        • Usage example
        • Config parameters
      • Datasets
        • DSTC2
        • Your data
      • Comparison
      • References
    • Open-Domain Question Answering
      • Task definition
      • Quick Start
      • Languages
      • Models
      • Running ODQA
        • Training
        • Interacting
      • Configuration
      • Comparison
      • References
    • Pattern Matching
    • Sequence-To-Sequence Dialogue Bot
      • Intro
      • Configs
      • Usage
      • Config parameters:
        • Comparison
      • References
    • Frequently Asked Questions Answering
      • Quick Start
        • Building
        • Inference
      • Config
        • Config Structure
        • Vectorizers
        • Classifiers for FAQ
      • Running FAQ
        • Training
        • Interacting
      • Available Data and Pretrained Models
    • eCommerce Bot
      • Quick Start
        • Building
        • Inference
      • Usage
        • Config file
        • Usage example
      • Configuration settings
        • eCommerce bot with BLEU-based ranker
        • eCommerce bot with TfIdf-based ranker
      • References
    • AIML
      • Quick Start
        • Usage
    • DSL
      • Quick Start
        • Usage
  • Pre-trained embeddings
    • BERT
      • License
      • Downloads
    • ELMo
      • License
      • Downloads
    • fastText
      • License
      • Downloads
      • Word vectors training parameters
  • AutoML
    • Cross-validation
      • Parameters
      • Special parameters in config
      • Results
    • Parameters evolution for DeepPavlov models
      • Example

Integrations

  • REST API
    • Advanced configuration
  • Telegram integration
    • Command line interface
    • Python
  • Yandex Alice integration
    • Command line interface
    • Python
  • Amazon Alexa integration
    • 1. Skill setup
    • 2. DeepPavlov skill/model REST service mounting
  • Microsoft Bot Framework integration
    • 1. Web App Bot setup
    • 2. DeepPavlov skill/model REST service mounting
  • Amazon AWS deployment
    • 1. AWS EC2 machine launch
    • 2. DeepPavlov ODQA deployment
    • 3. Accessing your ODQA API
  • Deeppavlov settings
    • 1. Settings files access and management
    • 2. Dialog logging
    • 3. Environment variables

Developer Guides

  • Contribution guide
  • Registry your model

Package Reference

  • agents
    • deeppavlov.agents.default_agent
    • deeppavlov.agents.filters
    • deeppavlov.agents.hello_bot_agent
    • deeppavlov.agents.processors
    • deeppavlov.agents.rich_content
  • core
    • deeppavlov.core.agent
    • deeppavlov.core.commands
    • deeppavlov.core.common
    • deeppavlov.core.data
    • deeppavlov.core.models
    • deeppavlov.core.skill
    • deeppavlov.core.trainers
  • dataset_iterators
  • dataset_readers
  • metrics
  • models
    • deeppavlov.models.api_requester
    • deeppavlov.models.bert
    • deeppavlov.models.classifiers
    • deeppavlov.models.doc_retrieval
    • deeppavlov.models.elmo
    • deeppavlov.models.embedders
    • deeppavlov.models.go_bot
    • deeppavlov.models.kbqa
    • deeppavlov.models.morpho_tagger
    • deeppavlov.models.ner
    • deeppavlov.models.preprocessors
    • deeppavlov.models.ranking
    • deeppavlov.models.seq2seq_go_bot
    • deeppavlov.models.sklearn
    • deeppavlov.models.slotfill
    • deeppavlov.models.spelling_correction
    • deeppavlov.models.squad
    • deeppavlov.models.tokenizers
    • deeppavlov.models.vectorizers
  • skills
    • deeppavlov.skills.aiml_skill
    • deeppavlov.skills.default_skill
    • deeppavlov.skills.dsl_skill
    • deeppavlov.skills.ecommerce_skill
    • deeppavlov.skills.pattern_matching_skill
  • vocabs
DeepPavlov
  • Docs »
  • agents
  • Edit on GitHub

agentsΒΆ

Agent classes.

Agents

  • deeppavlov.agents.default_agent
  • deeppavlov.agents.filters
  • deeppavlov.agents.hello_bot_agent
  • deeppavlov.agents.processors
  • deeppavlov.agents.rich_content
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